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Course Description

Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence

Course Outlines

The following primary topics are covered:
– Machine Learning Business and Technology Drivers
– Machine Learning Benefits and Challenges
– Machine Learning Usage Scenarios
– Datasets, Structured, Unstructured and Semi-Structured Data
– Models, Algorithms, Model Training and Learning
– How Machine Learning Works
– Collecting and Pre-Processing Training Data
– Algorithm and Model Selection
– Training Models and Deploy Trained Models
– Machine Learning Algorithms and Practices
– Supervised Learning, Classification, Decision Tree
– Regression, Ensemble Methods, Dimension Reduction
– Unsupervised Learning and Clustering
– Semi-Supervised and Reinforcement Learning
– Machine Learning Best Practices
– How Machine Learning Systems Work
– Common Machine Learning Mechanisms
– How Mechanisms Are Used in Model Training
– Machine Learning and Deep Learning, Artificial Intelligence (AI)

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